Your Guide To Land A Data Job In 1 Month

Note: This is an experimental plan with no success rate right now. If you're willing to work in it, make sure you give your 100%

Image By Author

It’s all about your effort.

I know the person in the picture above looks old after 1 month but that’s the best match I could find on Canva.

Anyways, what’s coming next is more important and boy oh boy am I excited about it.
I’ve decided to make a 1-month plan for landing an entry-level job in data. I’ve given that plan to 3 people to act on and hopefully, with a little bit of luck and a lot of effort we’ll see if they land a job in 1 month. Even if they don’t, they’ll be close to the goal. I’m gonna be sharing that plan ahead for all of you to follow as well, however, I won’t be guiding you personally, and it’s your wish whether to follow that plan or not. Here are the 3 people and a little bit about them:

1. Jake Tangonan

   Jake is an online grocer at Walmart in Las Vegas. He’s recently decided to shift to the data field and is looking for opportunities in BI/Data Analytics.

2. Rhona E.
   Rhona, previously a salesperson from the Philippines has also decided to shift towards data and is currently looking for opportunities in BI/Data Analytics.

3. Afolabi Olawale
   Afolabi, originally from Nigeria has recently decided to pursue data and is looking for opportunities in Data Science/Data Analytics.

Let’s wish them luck as today was their first day following the plan. Let’s hope they end with jobs by the end of the month (13th Feb) as nothing would make me happier than seeing them getting data jobs and my plan succeeding. If any of you have some opportunities, please do let me know as not all 3 are on substack so I can tell them as well.

Now let’s talk about the big plan.

The Big Plan

I’m gonna be listing down the big plan below, so prepare to be enlightened.

Week 1: Building SQL, Python Foundations and Networking

1. Practice Basic SQL Commands and Statements

   • Focus on foundational SQL commands like SELECT, WHERE, JOIN, GROUP BY, HAVING, and ORDER BY. You can also follow my post ‘The SQL Essentials’ to figure out what statements to learn first.

   • Build Python foundations if the role you’re looking for requires python

   • Explore real-world datasets and practice creating queries to extract meaningful insights. Use Maven Analytics or Kaggle for sample datasets

2. Connect with 1 Professional Daily on LinkedIn

   • Search for data professionals such as data analysts, BI engineers, or hiring managers.

   • Personalize your connection requests with a brief introduction and mention your shared interests in data.

   • Here’s how to drop them an introductory message:
     ” Hi, I’m ______ with an expertise in _____. I have done ________ projects and employed my skills of _____,_____,_____ etc and gained fruitful results in the form of a portfolio. I see you’re in this field as well, what is your opinion on using _____ for ______ results? (In the last 2 blanks, write something related to your field that you would want their opinion on, this would engage them instead of just seeing your introduction).

     Now chances are that they might not reply, but if they do, I can guarantee you’ll have a very good discussion related to your field.
     For example, My intro messages would be something like this:

     
     Hi I’m a BI Engineer working at a Data and AI firm called Addo.ai. My current expertise is in Apache Superset, however I have also completed some projects on Power BI and Tableau as practice. Here’s an interesting question, do you prefer Power BI or Tableau if given the choice between these tools? I personally would prefer Tableau but I’d love to hear your opinion as well.

3. Daily LinkedIn Post

   • Share your SQL and Python learning journey. For example, write posts about how you used specific commands and the insights they provided. Even if you use Select and From statements, do post about them, take a screenshot of your code, and post it with it. Document everything you do on Linkedin while learning.

   • Use hashtags like #SQL, #DataAnalytics, #DataJobs, and #LearningJourney to increase visibility.

Week 2: BI Tools and Business Acumen

1. Practice a BI Tool of Your Choice

   • Choose tools like Power BI, Tableau, or Apache Superset.

   • You will need someone to help you set up Apache Superset because it requires expertise in Docker. However, I do recommend Superset as it is a great tool to practice your business sense and logic, and it’s FREE!

   • Learn how to import datasets, clean data, and create dashboards.

2. Develop Business Sense Through Visualizations

   • Work on exercises like creating dashboards for sales trends, customer segmentation, or financial performance.

   • Choose a dataset and think for about 5-10 minutes, what KPIs would this business need. Then use chatgpt to get KPIs for that specific business dataset. Match your KPIs with the response.

   • Define business KPIs (e.g., revenue growth, customer churn, average order value) and build dashboards to track them.

Example Exercise:

2. Take a dataset on online retail sales and do the following:

   • Look at the data for 5-10 minutes, study it, just keep looking at it. Did any KPIs come to your mind? No, Google the business niche that the dataset is from, and search what their most important KPIs are. Then ask ChatGPT.

   • Figure out the most important KPIs for that business.

   • Create a dashboard on it.

3. Daily LinkedIn Connection

   • Continue connecting with professionals in your field, especially those experienced in BI tools.

4. Daily LinkedIn Post

   • Share your dashboards and the stories behind the data. Highlight the business insights your visualizations reveal. Post it on Linkedin, and scream to the world that you created a dashboard.

   • Use hashtags like #BusinessIntelligence, #PowerBI, #Tableau, and #DataVisualization.

Week 3: Resume Building and Job Applications

1. Build a Tailored Resume

   • Create a resume showcasing your SQL, Python, and BI tool skills.

   • Highlight completed projects, such as SQL queries, Python code, or BI dashboards.

   • Use quantifiable achievements, like “Built a dashboard to track KPIs, reducing manual reporting time by 30%.”

2. Create Role-Specific Resumes

   • Adjust your resume for different analyst roles (e.g., data analyst, sales analyst, financial analyst).

   • Make different copies of your resume for different roles

   • Emphasize role-relevant skills and projects, such as financial dashboards for finance roles or customer segmentation for sales roles.

3. Start Job Applications

   • Apply to 3-5 jobs daily. Focus on entry-level data roles, internships, and contract work.

   • Reach out to the job poster or a team member to express your interest in the role.

   • Go for internships leading to job opportunities, as they will contribute greatly to your learning.

   • If you get an interview, start preparing from YouTube, or wherever you can. That’s your green light, you’re getting somewhere.

   • Here are some websites for data jobs:
     Data Freelance Hub By Ashley Copp
     Find A Data Job By Avery Smith
     I’m following both of them on Linkedin and they have some great content you can consume and learn from. I vouch for their sites and if you contact Ashley and are from the UK or US he’ll help you himself.

4. Continue Daily LinkedIn Connections

   • Maintain consistent networking with professionals in your field.

Week 4: Intensify Applications and Networking

1. Focus on Job Applications

   • Increase your applications to 10-15 jobs daily.

   • Look for internships, entry-level roles, or freelance opportunities to gain experience.

2. Daily LinkedIn Connections and Posts

   • Keep connecting with professionals and sharing insights from your job search and learnings.

   • Post about interview experiences or lessons learned, maintaining an active presence.

Final Note:

By the end of this plan, you should have:

• Improved related skills with tangible projects.

• A tailored resume ready for multiple roles.

• A professional network to support your job search. 30 days, 30 meaningful connections.

Remember, internships are a great way to gain real-world experience. Stay consistent, refine your approach as needed, and focus on showcasing your enthusiasm for learning and problem-solving.

This is a fail-proof plan. It means that you might not succeed but will also not fail. I’ve listed what you’ll have after a month. I recommended Jake to post something about his learning journey on Linkedin and within a day he got 10k impressions. Now multiply 10% of that by 30 and you get 30k impressions in a month. Let’s say you got a 1% on your impressions, that makes 300 responses or people who engaged with your content. You’re going to benefit from that.

If this plan succeeds I’ll be taking on 5 more people to help land a data job but it will be paid as I’ll be putting in extra effort and time into it. Don’t worry it will still be a negotiable price as the audience is not always from a country with great purchasing power.

For now, to the audience, I would say,

Start Querying Today!

---

I am a BI Engineer working at a Data And AI Firm. I’m trying to help newbies in Data land their first data job and impact as many fellow data professionals as I can. Here’s my contact info:

1. ajmal7809@gmail.com

2. Linkedin

3. bideveloper_ (Discord)